Genome sequencing and next-generation sequence data analysis: A comprehensive compilation of bioinformatics tools and databases

AbstractLaboratory strains, cell lines, and other genetic materials change hands frequently in the life sciences. Despite evidence that such materials are subject to mix-ups, contamination, and accumulation of secondary mutations, verification of strains and samples is not an established part of many experimental workflows. With the plummeting cost of next generation technologies, it is conceivable that whole genome sequencing (WGS) could be applied to routine strain and sample verification in the future. To demonstrate the need for strain validation by WGS, we sequenced haploid yeast segregants derived from a popular commercial mutant collection and identified several unexpected mutations. We determined that available bioinformatics tools may be ill-suited for verification and highlight the importance of finishing reference genomes for commonly-used laboratory strains.

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Next-Generation Sequencing and Sequence Data Analysis

10.2174/97816810809251150101 ◽

2015 ◽

Cited By ~ 1

Keyword(s):

Data Analysis ◽

Next Generation Sequencing ◽

Sequence Data ◽

Next Generation ◽

Generation Sequencing

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High-performance integrated virtual environment (HIVE): a robust infrastructure for next-generation sequence data analysis

Database ◽

10.1093/database/baw022 ◽

2016 ◽

Vol 2016 ◽

pp. baw022 ◽

Cited By ~ 27

Author(s):

Vahan Simonyan ◽

Konstantin Chumakov ◽

Hayley Dingerdissen ◽

William Faison ◽

Scott Goldweber ◽

...

Keyword(s):

Data Analysis ◽

Virtual Environment ◽

High Performance ◽

Sequence Data ◽

Next Generation

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From Reactive Seismic Monitoring to Proactive Reservoir Management. How Network Densification and “Next-Generation” Data Analysis Empowers Effective Extraction and Disposal

Proceedings of the 2020 Latin America Unconventional Resources Technology Conference ◽

10.15530/urtec-2020-1387 ◽

2020 ◽

Author(s):

Adam Baig ◽

Sepideh Karimi

Keyword(s):

Data Analysis ◽

Reservoir Management ◽

Seismic Monitoring ◽

Next Generation

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ADVANCES IN THE USE OF NEXT-GENERATION SEQUENCE DATA IN PLANT SYSTEMATICS AND EVOLUTION

Acta Horticulturae ◽

10.17660/actahortic.2010.859.23 ◽

2010 ◽

pp. 193-206 ◽

Cited By ~ 3

Author(s):

D.E. Soltis ◽

G. Burleigh ◽

W.B. Barbazuk ◽

M.J. Moore ◽

P.S. Soltis

Keyword(s):

Sequence Data ◽

Plant Systematics ◽

Next Generation

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Whole-Genome Sequencing for Investigating a Health Care-Associated Outbreak of Carbapenem-Resistant Acinetobacter baumannii

Diagnostics ◽

10.3390/diagnostics11020201 ◽

2021 ◽

Vol 11 (2) ◽

pp. 201

Author(s):

Sang Mee Hwang ◽

Hee Won Cho ◽

Tae Yeul Kim ◽

Jeong Su Park ◽

Jongtak Jung ◽

...

Keyword(s):

Whole Genome Sequencing ◽

Acinetobacter Baumannii ◽

Genome Sequencing ◽

Snp Analysis ◽

Whole Genome ◽

Phylogenetic Tree Analysis ◽

Web Based ◽

Hospital Outbreak ◽

Carbapenem Resistant ◽

Bioinformatics Tools

Carbapenem-resistant Acinetobacter baumannii (CRAB) outbreaks in hospital settings challenge the treatment of patients and infection control. Understanding the relatedness of clinical isolates is important in distinguishing outbreak isolates from sporadic cases. This study investigated 11 CRAB isolates from a hospital outbreak by whole-genome sequencing (WGS), utilizing various bioinformatics tools for outbreak analysis. The results of multilocus sequence typing (MLST), single nucleotide polymorphism (SNP) analysis, and phylogenetic tree analysis by WGS through web-based tools were compared, and repetitive element polymerase chain reaction (rep-PCR) typing was performed. Through the WGS of 11 A. baumannii isolates, three clonal lineages were identified from the outbreak. The coexistence of blaOXA-23, blaOXA-66, blaADC-25, and armA with additional aminoglycoside-inactivating enzymes, predicted to confer multidrug resistance, was identified in all isolates. The MLST Oxford scheme identified three types (ST191, ST369, and ST451), and, through whole-genome MLST and whole-genome SNP analyses, different clones were found to exist within the MLST types. wgSNP showed the highest discriminatory power with the lowest similarities among the isolates. Using the various bioinformatics tools for WGS, CRAB outbreak analysis was applicable and identified three discrete clusters differentiating the separate epidemiologic relationships among the isolates.

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